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| The Effects of Task Dependencies on Human-Bot Collaborative Patterns in Online Knowledge Communities: An Empirical Study from a Machine Behavior Perspective |
| Zuo Min1, Qiu Jiangnan2 |
1.Business School, Shandong University of Technology, Zibo 255000
2.School of Economics and Management, Dalian University of Technology, Dalian 116024 |
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Abstract Bots have been introduced into online knowledge communities (OKC) to achieve human-bot intelligence enhancement, and the key is to understand the interdependence between humans and bots performing tasks, as well as the human-bot collaborative pattern that coordinates teamwork. From the perspective of machine behavior, this study uses process mining methods to identify and subdivide human-bot collaboration patterns into two categories: automated and enhanced assistance. Based on coordination theory, a dual fixed-effects model was used to empirically analyze the impact of three basic task dependencies-flow, integration, and sharing-on human-bot collaborative patterns, as well as the moderating effect of task types. The results indicate that the enhanced assistance collaborative pattern of bot-assisted humans effectively manages task-dependent human-bot team coordination problems, whereas the role of the automated assistance collaborative pattern that is independently executed by bots is limited. Notably, the above relationships change depending on task type. This study expands the application of coordination theory and machine behavior, enriches the empirical research on the factors influencing human-bot collaborative patterns, and provides useful guidance for managing human-bot processes and designing tasks on the OKC platform.
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Received: 23 May 2024
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